Frozen Bodies and Future Imaginaries: Assisted Dying, Cryonics, and a Good Death
Bibliographic record
Abstract
In October of 2018, Norman Hardy became the first individual to be cryopreserved after successful recourse to California’s then recently passed End of Life Options Act. This was a right not afforded to Thomas Donaldson, who in 1993 was legally denied the ability to end his own life before a tumor irreversibly destroyed his brain tissue. The cases of Norman Hardy and Thomas Donaldson reflect ethical and moral issues common to the practice of assisted dying, but unique to cryonics. In this essay, I explore the intersections between ideologies of immortality and assisted dying among two social movements with seemingly opposing epistemologies: cryonicists and medical aid in dying (MAiD) advocates. How is MAiD understood among cryonicists, and how has it been deployed by cryonicists in the United States? What are the historical and cultural circumstances that have made access to euthanasia a moral necessity for proponents of cryonics and MAiD? In this comparative essay, I examine the similarities between the biotechnological and future imaginaries of cryonics and MAiD. I aim to show that proponents of both practices are in search of a good death, and how both conceptualize dying as an ethical good. Cryonics members and terminal patients constitute unique biosocial worlds, which can intersect in unconventional ways. As temporalizing practices, both cryonics and MAiD reflect a will to master the time and manner of death.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".